A Business Intelligence Framework for Short-Term Consumer
Demand Forecasting Using Public Macroeconomic Indicators
Ilhan Ozturk1,∗
1College of Business Administration, University of Sharjah, UAE
Email: iozturk@sharjah.ac.ae
Abstract
Business intelligence has emerged to be a high-level managerial competency among organizations that aim to
enhance the quality of planning, responsiveness in operations and evidence-based decision making in uncertain
market environments. Short-term demand forecasting is one of its most important business applications since
fluctuations in demand expectations affect budgeting, inventory planning, staffing, procurement timing and
revenue management. The paper formulates and tests a business intelligence system of consumer demand
prediction over short-term with the help of the public macroeconomic variables. It aims to show how external
economic signals may be converted into an explainable, reproducible, and useful forecasting layer to be used
in dashboards and decision support systems. The research forecasts next-period real consumer spending using
lagged indicators based on output, disposable income, investment, unemployment, inflation, and short-term
interest rates using a publicly available U.S. macroeconomic data, which is periodically updated. Ordinary
least squares, ridge regression, random forest and gradient boosting are compared by using a chronological
holdout design. The empirical findings indicate that the regression-based models that are interpretable have
the best out-of-sample performance, and ordinary least squares model has the lowest error and greatest
explanatory power. The results suggest that effective business forecasting support can be offered using
transparent analytics without the need to use complex black-box models. The study is valuable because
it adds to the body of business intelligence literature a reproducible external-signal prediction pipeline, a
comparison of the explainable and non-explainable models in a management context, and a translation of the
forecasting results into operational and strategic planning consequences.
Keywords: Business intelligen